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Weakly supervised learning of single-cell feature embeddings.
Caicedo JC, McQuin C, Goodman A, S S, AE C. Weakly supervised learning of single-cell feature embeddings. . In: 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) .; Submitted. doi:10.1101/293431.
Automating Morphological Profiling with Generic Deep Convolutional Networks.
Pawlowski N, Caicedo JC, Singh S, Carpenter AE, Storkey A. Automating Morphological Profiling with Generic Deep Convolutional Networks. Submitted. doi:10.1101/085118.
Inter-laboratory automation of the in vitro micronucleus assay using imaging flow cytometry and deep learning.
Wills JW, Verma JR, Rees BJ, et al. Inter-laboratory automation of the in vitro micronucleus assay using imaging flow cytometry and deep learning. Arch Toxicol. 2021;95(9):3101-3115. doi:10.1007/s00204-021-03113-0.
Deepometry, a framework for applying supervised and weakly supervised deep learning to imaging cytometry.
Doan M, Barnes C, McQuin C, et al. Deepometry, a framework for applying supervised and weakly supervised deep learning to imaging cytometry. Nat Protoc. 2021;16(7):3572-3595. doi:10.1038/s41596-021-00549-7.
Resolving cell state in iPSC-derived human neural samples with multiplexed fluorescence imaging.
Tomov ML, O'Neil A, Abbasi HS, et al. Resolving cell state in iPSC-derived human neural samples with multiplexed fluorescence imaging. Commun Biol. 2021;4(1):786. doi:10.1038/s42003-021-02276-x.
Which image-based phenotypes are most promising for using AI to understand cellular functions and why?
Lundberg E, Funke J, Uhlmann V, et al. Which image-based phenotypes are most promising for using AI to understand cellular functions and why?. Cell Syst. 2021;12(5):384-387. doi:10.1016/j.cels.2021.04.012.
ImageJ and CellProfiler: Complements in Open-Source Bioimage Analysis.
Dobson ETA, Cimini B, Klemm AH, Wählby C, Carpenter AE, Eliceiri KW. ImageJ and CellProfiler: Complements in Open-Source Bioimage Analysis. Curr Protoc. 2021;1(5):e89. doi:10.1002/cpz1.89.
Developing open-source software for bioimage analysis: opportunities and challenges.
Levet F, Carpenter AE, Eliceiri KW, Kreshuk A, Bankhead P, Haase R. Developing open-source software for bioimage analysis: opportunities and challenges. F1000Res. 2021;10:302. doi:10.12688/f1000research.52531.1.
Diagnostic Potential of Imaging Flow Cytometry.
Doan M, Vorobjev I, Rees P, et al. Diagnostic Potential of Imaging Flow Cytometry. Trends Biotechnol. 2018;36(7):649-652. doi:10.1016/j.tibtech.2017.12.008.
CellProfiler 3.0: Next-generation image processing for biology.
McQuin C, Goodman A, Chernyshev V, et al. CellProfiler 3.0: Next-generation image processing for biology. PLoS Biol. 2018;16(7):e2005970. doi:10.1371/journal.pbio.2005970.
Repurposing High-Throughput Image Assays Enables Biological Activity Prediction for Drug Discovery.
Simm J, Klambauer G, Arany A, et al. Repurposing High-Throughput Image Assays Enables Biological Activity Prediction for Drug Discovery. Cell Chem Biol. 2018;25(5):611-618.e3. doi:10.1016/j.chembiol.2018.01.015.
Opportunities and obstacles for deep learning in biology and medicine.
Ching T, Himmelstein DS, Beaulieu-Jones BK, et al. Opportunities and obstacles for deep learning in biology and medicine. J R Soc Interface. 2018;15(141). doi:10.1098/rsif.2017.0387.
Combining morphological and migration profiles of in vitro time-lapse data.
Becker T, Caicedo JC, Singer S, weckmann M, AE C. Combining morphological and migration profiles of in vitro time-lapse data. In: IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).; Submitted.
Quality Control for High-Throughput Imaging Experiments Using Machine Learning in Cellprofiler.
Bray M-A, Carpenter AE. Quality Control for High-Throughput Imaging Experiments Using Machine Learning in Cellprofiler. Methods Mol Biol. 2018;1683:89-112. doi:10.1007/978-1-4939-7357-6_7.
Designed Surface Topographies Control ICAM-1 Expression in Tonsil-Derived Human Stromal Cells.
Vasilevich AS, Mourcin F, Mentink A, et al. Designed Surface Topographies Control ICAM-1 Expression in Tonsil-Derived Human Stromal Cells. Front Bioeng Biotechnol. 2018;6:87. doi:10.3389/fbioe.2018.00087.
A dataset of images and morphological profiles of 30 000 small-molecule treatments using the Cell Painting assay.
Bray M-A, Gustafsdottir SM, Rohban MH, et al. A dataset of images and morphological profiles of 30 000 small-molecule treatments using the Cell Painting assay. Gigascience. 2017;6(12):1-5. doi:10.1093/gigascience/giw014.
Reconstructing cell cycle and disease progression using deep learning.
Eulenberg P, Köhler N, Blasi T, et al. Reconstructing cell cycle and disease progression using deep learning. Nat Commun. 2017;8(1):463. doi:10.1038/s41467-017-00623-3.
Data-analysis strategies for image-based cell profiling.
Caicedo JC, Cooper S, Heigwer F, et al. Data-analysis strategies for image-based cell profiling. Nat Methods. 2017;14(9):849-863. doi:10.1038/nmeth.4397.
Mining for osteogenic surface topographies: In silico design to in vivo osseo-integration.
Hulshof FFB, Papenburg B, Vasilevich A, et al. Mining for osteogenic surface topographies: In silico design to in vivo osseo-integration. Biomaterials. 2017;137:49-60. doi:10.1016/j.biomaterials.2017.05.020.
Systematic, multiparametric analysis of Mycobacterium tuberculosis intracellular infection offers insight into coordinated virulence.
Barczak AK, Avraham R, Singh S, et al. Systematic, multiparametric analysis of Mycobacterium tuberculosis intracellular infection offers insight into coordinated virulence. PLoS Pathog. 2017;13(5):e1006363. doi:10.1371/journal.ppat.1006363.
Systematic morphological profiling of human gene and allele function via Cell Painting.
Rohban MHossein, Singh S, Wu X, et al. Systematic morphological profiling of human gene and allele function via Cell Painting. Elife. 2017;6. doi:10.7554/eLife.24060.
Applying Faster R-CNN for Object Detection on Malaria Images.
Hung J, Goodman A, lopes S, et al. Applying Faster R-CNN for Object Detection on Malaria Images. . In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).; Submitted.
CytoGAN: Generative Modeling of Cell Images
Goldsborough P, Pawlowski N, Caicedo JC, Singh S, Carpenter AE. CytoGAN: Generative Modeling of Cell Images. In: Workshop on Machine Learning in Computational Biology, Neural Information Processing Systems. . Workshop on Machine Learning in Computational Biology, Neural Information Processing Systems. .; Submitted. doi:https://doi.org/10.1101/227645.
An open-source solution for advanced imaging flow cytometry data analysis using machine learning.
Hennig H, Rees P, Blasi T, et al. An open-source solution for advanced imaging flow cytometry data analysis using machine learning. Methods. 2017;112:201-210. doi:10.1016/j.ymeth.2016.08.018.
Workshop on Machine Learning in Computational Biology, Neural Information Processing Systems.
Goldsborough P, Pawlowski N, Caicedo JC, Singh S, Carpenter AE. Workshop on Machine Learning in Computational Biology, Neural Information Processing Systems. In: Workshop on Machine Learning in Computational Biology, Neural Information Processing Systems. Workshop on Machine Learning in Computational Biology, Neural Information Processing Systems.; Submitted. doi:http://dx.doi.org/10.1101/227645.
Imagining the future of bioimage analysis.
Meijering E, Carpenter AE, Peng H, Hamprecht FA, Olivo-Marin J-C. Imagining the future of bioimage analysis. Nat Biotechnol. 2016;34(12):1250-1255. doi:10.1038/nbt.3722.
High-Throughput Platform for Identifying Molecular Factors Involved in Phenotypic Stabilization of Primary Human Hepatocytes In Vitro.
Shan J, Logan DJ, Root DE, Carpenter AE, Bhatia SN. High-Throughput Platform for Identifying Molecular Factors Involved in Phenotypic Stabilization of Primary Human Hepatocytes In Vitro. J Biomol Screen. 2016;21(9):897-911. doi:10.1177/1087057116660277.
Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes.
Bray M-A, Singh S, Han H, et al. Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes. Nat Protoc. 2016;11(9):1757-74. doi:10.1038/nprot.2016.105.
QSAR-Driven Discovery of Novel Chemical Scaffolds Active against Schistosoma mansoni.
Melo-Filho CC, Dantas RF, Braga RC, et al. QSAR-Driven Discovery of Novel Chemical Scaffolds Active against Schistosoma mansoni. J Chem Inf Model. 2016;56(7):1357-72. doi:10.1021/acs.jcim.6b00055.
CellProfiler Analyst: interactive data exploration, analysis and classification of large biological image sets.
Dao D, Fraser AN, Hung J, Ljosa V, Singh S, Carpenter AE. CellProfiler Analyst: interactive data exploration, analysis and classification of large biological image sets. Bioinformatics. 2016. doi:10.1093/bioinformatics/btw390.
An open-source computational tool to automatically quantify immunolabeled retinal ganglion cells.
Dordea AC, Bray M-A, Allen K, et al. An open-source computational tool to automatically quantify immunolabeled retinal ganglion cells. Exp Eye Res. 2016;147:50-6. doi:10.1016/j.exer.2016.04.012.
Quantifying co-cultured cell phenotypes in high-throughput using pixel-based classification.
Logan DJ, Shan J, Bhatia SN, Carpenter AE. Quantifying co-cultured cell phenotypes in high-throughput using pixel-based classification. Methods. 2016;96:6-11. doi:10.1016/j.ymeth.2015.12.002.
CP-CHARM: segmentation-free image classification made accessible.
Uhlmann V, Singh S, Carpenter AE. CP-CHARM: segmentation-free image classification made accessible. BMC Bioinformatics. 2016;17:51. doi:10.1186/s12859-016-0895-y.
A Genome-wide RNAi Screen for Microtubule Bundle Formation and Lysosome Motility Regulation in Drosophila S2 Cells.
Jolly AL, Luan C-H, Dusel BE, et al. A Genome-wide RNAi Screen for Microtubule Bundle Formation and Lysosome Motility Regulation in Drosophila S2 Cells. Cell Rep. 2016;14(3):611-20. doi:10.1016/j.celrep.2015.12.051.
Label-free cell cycle analysis for high-throughput imaging flow cytometry.
Blasi T, Hennig H, Summers HD, et al. Label-free cell cycle analysis for high-throughput imaging flow cytometry. Nat Commun. 2016;7:10256. doi:10.1038/ncomms10256.
CellProfiler Tracer: exploring and validating high-throughput, time-lapse microscopy image data.
Bray M-A, Carpenter AE. CellProfiler Tracer: exploring and validating high-throughput, time-lapse microscopy image data. BMC Bioinformatics. 2015;16:368. doi:10.1186/s12859-015-0759-x.
CDy6, a photostable probe for long-term real-time visualization of mitosis and proliferating cells.
Jeong Y-M, Duanting Z, Hennig H, et al. CDy6, a photostable probe for long-term real-time visualization of mitosis and proliferating cells. Chem Biol. 2015;22(2):299-307. doi:10.1016/j.chembiol.2014.11.018.
Using CellProfiler for Automatic Identification and Measurement of Biological Objects in Images.
Bray M-A, Vokes MS, Carpenter AE. Using CellProfiler for Automatic Identification and Measurement of Biological Objects in Images. Curr Protoc Mol Biol. 2015;109:14.17.1-13. doi:10.1002/0471142727.mb1417s109.
Morphological Profiles of RNAi-Induced Gene Knockdown Are Highly Reproducible but Dominated by Seed Effects.
Singh S, Wu X, Ljosa V, et al. Morphological Profiles of RNAi-Induced Gene Knockdown Are Highly Reproducible but Dominated by Seed Effects. PLoS One. 2015;10(7):e0131370. doi:10.1371/journal.pone.0131370.
Crowdsourcing the creation of image segmentation algorithms for connectomics.
Arganda-Carreras I, Turaga SC, Berger DR, et al. Crowdsourcing the creation of image segmentation algorithms for connectomics. Front Neuroanat. 2015;9:142. doi:10.3389/fnana.2015.00142.
Increased expression of the immune modulatory molecule PD-L1 (CD274) in anaplastic meningioma.
Du Z, Abedalthagafi M, Aizer AA, et al. Increased expression of the immune modulatory molecule PD-L1 (CD274) in anaplastic meningioma. Oncotarget. 2014. Available at: http://www.impactjournals.com/oncotarget/misc/linkedout.php?pii=3082.
ProtocolNavigator: emulation-based software for the design, documentation and reproduction biological experiments.
Khan IA, Fraser A, Bray M-A, et al. ProtocolNavigator: emulation-based software for the design, documentation and reproduction biological experiments. Bioinformatics. 2014;30(23):3440-2. doi:10.1093/bioinformatics/btu554.
Pipeline for illumination correction of images for high-throughput microscopy.
Singh S, Bray M-A, Jones TR, Carpenter AE. Pipeline for illumination correction of images for high-throughput microscopy. J Microsc. 2014;256(3):231-6. doi:10.1111/jmi.12178.
Nanoparticle vesicle encoding for imaging and tracking cell populations.
Rees P, Wills JW, M Brown R, et al. Nanoparticle vesicle encoding for imaging and tracking cell populations. Nat Methods. 2014;11(11):1177-81. doi:10.1038/nmeth.3105.
High- and low-throughput scoring of fat mass and body fat distribution in C. elegans.
Wählby C, Conery ALee, Bray M-A, et al. High- and low-throughput scoring of fat mass and body fat distribution in C. elegans. Methods. 2014;68(3):492-9. doi:10.1016/j.ymeth.2014.04.017.
Increasing the Content of High-Content Screening: An Overview.
Singh S, Carpenter AE, Genovesio A. Increasing the Content of High-Content Screening: An Overview. J Biomol Screen. 2014;19(5):640-50. doi:10.1177/1087057114528537.
Identification of host-targeted small molecules that restrict intracellular Mycobacterium tuberculosis growth.
Stanley SA, Barczak AK, Silvis MR, et al. Identification of host-targeted small molecules that restrict intracellular Mycobacterium tuberculosis growth. PLoS Pathog. 2014;10(2):e1003946. doi:10.1371/journal.ppat.1003946.
ZFHX4 interacts with the NuRD core member CHD4 and regulates the glioblastoma tumor-initiating cell state.
Chudnovsky Y, Kim D, Zheng S, et al. ZFHX4 interacts with the NuRD core member CHD4 and regulates the glioblastoma tumor-initiating cell state. Cell Rep. 2014;6(2):313-24. doi:10.1016/j.celrep.2013.12.032.
High content image analysis identifies novel regulators of synaptogenesis in a high-throughput RNAi screen of primary neurons.
Nieland TJF, Logan DJ, Saulnier J, et al. High content image analysis identifies novel regulators of synaptogenesis in a high-throughput RNAi screen of primary neurons. PLoS One. 2014;9(3):e91744. doi:10.1371/journal.pone.0091744.
Comparison of methods for image-based profiling of cellular morphological responses to small-molecule treatment.
Ljosa V, Caie PD, Horst RTer, et al. Comparison of methods for image-based profiling of cellular morphological responses to small-molecule treatment. J Biomol Screen. 2013;18(10):1321-9. doi:10.1177/1087057113503553.
Niche-based screening identifies small-molecule inhibitors of leukemia stem cells.
Hartwell KA, Miller PG, Mukherjee S, et al. Niche-based screening identifies small-molecule inhibitors of leukemia stem cells. Nat Chem Biol. 2013;9(12):840-848. doi:10.1038/nchembio.1367.
Identification of small molecules for human hepatocyte expansion and iPS differentiation.
Shan J, Schwartz RE, Ross NT, et al. Identification of small molecules for human hepatocyte expansion and iPS differentiation. Nat Chem Biol. 2013;9(8):514-20. doi:10.1038/nchembio.1270.
A microscale human liver platform that supports the hepatic stages of Plasmodium falciparum and vivax.
March S, Ng S, Velmurugan S, et al. A microscale human liver platform that supports the hepatic stages of Plasmodium falciparum and vivax. Cell Host Microbe. 2013;14(1):104-15. doi:10.1016/j.chom.2013.06.005.
Multiplex cytological profiling assay to measure diverse cellular states.
Gustafsdottir SM, Ljosa V, Sokolnicki KL, et al. Multiplex cytological profiling assay to measure diverse cellular states. PLoS One. 2013;8(12):e80999. doi:10.1371/journal.pone.0080999.
Identification of regulators of polyploidization presents therapeutic targets for treatment of AMKL.
Wen Q, Goldenson B, Silver SJ, et al. Identification of regulators of polyploidization presents therapeutic targets for treatment of AMKL. Cell. 2012;150(3):575-89. doi:10.1016/j.cell.2012.06.032.
Annotated high-throughput microscopy image sets for validation.
Ljosa V, Sokolnicki KL, Carpenter AE. Annotated high-throughput microscopy image sets for validation. Nat Methods. 2012;9(7):637. doi:10.1038/nmeth.2083.
A call for bioimaging software usability.
Carpenter AE, Kamentsky L, Eliceiri KW. A call for bioimaging software usability. Nat Methods. 2012;9(7):666-70. doi:10.1038/nmeth.2073.
An image analysis toolbox for high-throughput C. elegans assays.
Wählby C, Kamentsky L, Liu ZH, et al. An image analysis toolbox for high-throughput C. elegans assays. Nat Methods. 2012;9(7):714-6. doi:10.1038/nmeth.1984.
Workflow and metrics for image quality control in large-scale high-content screens.
Bray M-A, Fraser AN, Hasaka TP, Carpenter AE. Workflow and metrics for image quality control in large-scale high-content screens. J Biomol Screen. 2012;17(2):266-74. doi:10.1177/1087057111420292.
A chemical screen probing the relationship between mitochondrial content and cell size.
Kitami T, Logan DJ, Negri J, et al. A chemical screen probing the relationship between mitochondrial content and cell size. PLoS One. 2012;7(3):e33755. doi:10.1371/journal.pone.0033755.
Visualization of parameter space for image analysis.
A Pretorius J, Bray M-AP, Carpenter AE, Ruddle RA. Visualization of parameter space for image analysis. IEEE Trans Vis Comput Graph. 2011;17(12):2402-11. doi:10.1109/TVCG.2011.253.
Human tumors instigate granulin-expressing hematopoietic cells that promote malignancy by activating stromal fibroblasts in mice.
Elkabets M, Gifford AM, Scheel C, et al. Human tumors instigate granulin-expressing hematopoietic cells that promote malignancy by activating stromal fibroblasts in mice. J Clin Invest. 2011;121(2):784-99. doi:10.1172/JCI43757.
Screening cellular feature measurements for image-based assay development.
Logan DJ, Carpenter AE. Screening cellular feature measurements for image-based assay development. J Biomol Screen. 2010;15(7):840-6. doi:10.1177/1087057110370895.
Introduction to the quantitative analysis of two-dimensional fluorescence microscopy images for cell-based screening.
Ljosa V, Carpenter AE. Introduction to the quantitative analysis of two-dimensional fluorescence microscopy images for cell-based screening. PLoS Comput Biol. 2009;5(12):e1000603. doi:10.1371/journal.pcbi.1000603.
Ultrasome: efficient aberration caller for copy number studies of ultra-high resolution.
Nilsson B, Johansson M, Al-Shahrour F, Carpenter AE, Ebert BL. Ultrasome: efficient aberration caller for copy number studies of ultra-high resolution. Bioinformatics. 2009;25(8):1078-9. doi:10.1093/bioinformatics/btp091.
Scoring diverse cellular morphologies in image-based screens with iterative feedback and machine learning.
Jones TR, Carpenter AE, Lamprecht MR, et al. Scoring diverse cellular morphologies in image-based screens with iterative feedback and machine learning. Proc Natl Acad Sci U S A. 2009;106(6):1826-31. doi:10.1073/pnas.0808843106.
Extracting rich information from images.
Carpenter AE. Extracting rich information from images. Methods Mol Biol. 2009;486:193-211. doi:10.1007/978-1-60327-545-3_14.
CellProfiler Analyst: data exploration and analysis software for complex image-based screens.
Jones TR, Kang IHan, Wheeler DB, et al. CellProfiler Analyst: data exploration and analysis software for complex image-based screens. BMC Bioinformatics. 2008;9:482. doi:10.1186/1471-2105-9-482.
High-throughput screens for fluorescent dye discovery.
Ljosa V, Carpenter AE. High-throughput screens for fluorescent dye discovery. Trends Biotechnol. 2008;26(10):527-30. doi:10.1016/j.tibtech.2008.06.008.
Image-based chemical screening.
Carpenter AE. Image-based chemical screening. Nat Chem Biol. 2007;3(8):461-5. doi:10.1038/nchembio.2007.15.